🚀 Advance Data Science

A comprehensive collection of Advanced Data Science, Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Time Series Forecasting, and Generative AI projects developed during my Advanced Data Science learning journey.

This repository contains hands-on implementations of ANNs, CNNs, RNNs, LSTMs, Autoencoders, GANs, Transformers, T5, YOLO, XGBoost, and Forecasting techniques using real-world datasets and research-oriented experiments.

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📌 About

This repository serves as a centralized collection of notebooks, assignments, experiments, and projects completed while exploring advanced concepts in Data Science and Artificial Intelligence.

The work covers multiple domains including Machine Learning, Deep Learning, Natural Language Processing (NLP), Computer Vision, Time Series Forecasting, Generative AI, and Explainable AI. Each notebook focuses on a specific concept, algorithm, architecture, or real-world application and demonstrates practical implementation using Python and Google Colab.

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🎯 Repository Highlights

• Machine Learning & Predictive Analytics
• Deep Learning Architectures
• Natural Language Processing (NLP)
• Computer Vision Applications
• Time Series Forecasting
• Generative AI & Transformer Models
• Research-Oriented Experiments
• Google Colab Implementations

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📂 Repository Structure

🧠 Neural Network Fundamentals

• Activation Functions
• Optimization Techniques
• Regularization Techniques

🤖 Deep Learning

• Artificial Neural Networks (ANN)
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
• Long Short-Term Memory Networks (LSTM)
• Autoencoders
• Convolutional Autoencoders
• Variational Autoencoders (VAE)

🎨 Generative AI

• Generative Adversarial Networks (GAN)
• Deep Convolutional GAN (DCGAN)
• Wasserstein GAN (WGAN)
• Text & Image Generation

📝 Natural Language Processing

• English to Hindi Translation using Transformers
• English to Italian Translation using Seq2Seq + Attention
• Text Summarization using T5
• Gen-Z Slang Translation
• Transformer Architectures

🖼️ Computer Vision

• Oxford-IIIT Pet Classification using CNN
• Object Detection using YOLO

📈 Time Series Forecasting

• Fundamentals of Forecasting
• Understanding Time Series Data
• Exponential Smoothing Methods

💼 Machine Learning Projects

• Loan Approval Prediction using XGBoost

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🧠 Tech Stack

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🚀 Getting Started

Clone the repository:

git clone https://github.com/harshitt018/Advance-Data-Science.git

Open any notebook using:

• Google Colab
• Jupyter Notebook
• JupyterLab

Install required dependencies:

pip install tensorflow torch numpy pandas matplotlib scikit-learn transformers

Run the notebooks sequentially and explore different concepts in Data Science and Artificial Intelligence.

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📊 Topics Covered

Domain	Concepts
Machine Learning	XGBoost, Predictive Analytics
Deep Learning	ANN, CNN, RNN, LSTM
Computer Vision	YOLO, Image Classification
NLP	Transformers, Seq2Seq, T5
Generative AI	GAN, DCGAN, WGAN
Representation Learning	Autoencoders, VAEs
Forecasting	Time Series Analysis

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🎓 Learning Outcomes

Through these projects and experiments, I gained practical experience in:

• Machine Learning Model Development
• Deep Learning Architectures
• Transformer-Based NLP Systems
• Computer Vision Applications
• Time Series Forecasting
• Generative AI Techniques
• Model Evaluation & Optimization
• Research-Oriented Problem Solving

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👨‍💻 Author

Harshit Jaiswal

B.Sc. Information Technology Graduate

Data Science | Machine Learning | Deep Learning | Artificial Intelligence

Research Author – IJAIR 2026

Mumbai, India

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⭐ Support

If you find this repository useful, consider giving it a ⭐ on GitHub.

It helps others discover the repository and motivates future contributions and improvements.